Image processing apparatus and computer program

ABSTRACT

An image processing apparatus includes: a controller configured to perform: acquiring original image data representing an original image and corrected image data representing a corrected image, the corrected image data being generated by executing specific correction processing on the original image data; calculating a first index value and a second index value, the first index value relating to a color distribution of the original image by using the original image data, and the second index value relating to a color distribution of the corrected image by using the corrected image data; and determining which processing, among a plurality of processing candidates that are executable by the image processing apparatus, corresponds to the specific correction processing, based on comparison of the calculated first index value and the calculated second index value.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from Japanese Patent Application No.2015-065736 filed on Mar. 27, 2015, the entire subject matter of whichis incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to image processing of executing correctionprocessing for image data, which is generated by optically reading adocument.

BACKGROUND

A technology of performing correction processing for image data, whichis generated by optically reading a document with a reading apparatussuch as a scanner, has been known. For example, JP2006-339874A disclosesa technology of changing a value of a pixel indicative of a specificcolor in an image into a value of a pixel indicative of white. Accordingto this technology, a color palette is displayed on a multifunctionmachine color liquid crystal panel, so that an instruction of thespecific color is received from a user.

However, there is a variety of correction processing, in addition to theabove correction processing of changing the specific color into thewhite. It cannot be said that a user can easily select appropriatecorrection processing from the plural types of processing. Specifically,a user who does not know about the correction processing well may have adifficulty in selecting the appropriate correction processing.Therefore, it is required to reduce a trouble of the user who uses thecorrection processing.

SUMMARY

This disclosure provides a technology capable of reducing a trouble of auser who uses correction processing to be executed for image data.

The disclosed technology can be implemented as following applicationexamples.

An image processing apparatus of an application example of this discloseincludes a controller configured to perform: acquiring original imagedata representing an original image and corrected image datarepresenting a corrected image, the corrected image data being generatedby executing specific correction processing on the original image data;calculating a first index value and a second index value, the firstindex value relating to a color distribution of the original image byusing the original image data, and the second index value relating to acolor distribution of the corrected image by using the corrected imagedata; and determining which processing, among a plurality of processingcandidates that are executable by the image processing apparatus,corresponds to the specific correction processing, based on comparisonof the calculated first index value and the calculated second indexvalue.

According to the above configuration, it is possible to appropriatelydetermine which of plural types of candidates for processing that isexecutable by the image processing corresponds to the specificcorrection processing executed for the original image data, according tothe color distribution of the original image and the color distributionof the corrected image. As a result, for example, it is possible tonotify the determined correction processing to a user and toautomatically execute the determined correction processing, so that itis possible to reduce a trouble of the user who uses the correctionprocessing.

An image processing apparatus of another application example of thisdisclose includes a controller configured to perform: acquiring originalimage data representing an original image, corrected image datarepresenting a corrected image and target image data indicative of animage of a correction target, the corrected image data being generatedby executing specific correction processing on the original image data;determining which processing, among a plurality of processing candidatesthat are executable by the image processing apparatus, corresponds tothe specific correction processing, based on comparison of the originalimage data and the corrected image data; determining a parameter for theprocessing corresponding to the specific correction processing, thespecific correction processing being determined among the plurality ofthe processing candidates; and executing the processing determined amongthe plurality of the processing candidates for the target image data byusing the determined parameter.

According to the above configuration, it is possible to appropriatelydetermine to which of the plurality of processing candidates capable ofexecuting the image processing the specific correction processingexecuted for the original image data corresponds, based on thecomparison of the original image data and the corrected image data. Aparameter for the determined processing is determined, and thedetermined processing is executed for the target image data by using theparameter. As a result, it is possible to reduce a user's burden to setthe parameter and the type of the correction processing at the imageprocessing apparatus.

In the meantime, the disclosed technology can be implemented into avariety of forms, for example, a control device of an image readingapparatus, an image processing method, a computer program forimplementing functions of the apparatus and method, a recording mediumhaving the computer program recorded therein, and the like.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and additional features and characteristics of thisdisclosure will become more apparent from the following detaileddescriptions considered with the reference to the accompanying drawings,wherein:

FIG. 1 is a block diagram illustrating configurations of a secondmultifunction machine 200 and a first multifunction machine 100;

FIG. 2 is a flowchart and a table illustrating an outline of correctionprocessing;

FIGS. 3A and 3B illustrate examples of a tone curve;

FIGS. 4A to 4C illustrate an example of an original image and acorrected image;

FIG. 5 is a flowchart of correction information registration processing;

FIG. 6 illustrates an example of a result notification screen WI1;

FIG. 7 is a flowchart of correction type presumption processing;

FIGS. 8A and 8B illustrate an example of a histogram of an originalimage IA and a distribution area of the original image IA;

FIGS. 9A and 9B illustrate an example of a histogram of a correctedimage IB;

FIGS. 10A and 10B illustrate an example of the histogram of thecorrected image IB;

FIGS. 11A to 11D illustrate an example of a distribution area of thecorrected image IB;

FIG. 12 is a flowchart of background removal presumption processing;

FIG. 13 is a flowchart of ghosting removal presumption processing;

FIG. 14 is a flowchart of overall brightness correction presumptionprocessing;

FIG. 15 is a flowchart of contrast correction presumption processing;

FIG. 16 is a flowchart of correction level determination processing;

FIG. 17 is a flowchart of scan processing; and

FIGS. 18A and 18B illustrate an example of an instruction input screenWI2 and a detail setting screen WI3.

DETAILED DESCRIPTION A. Illustrative Embodiment A-1: Configuration ofMultifunction Machine

In the below, illustrative embodiments of this disclosure will bedescribed with reference to the drawings. FIG. 1 is a block diagramillustrating configurations of a second multifunction machine 200, whichis an image processing apparatus of an illustrative embodiment, and afirst multifunction machine 100, which is other image processingapparatus.

The second multifunction machine 200 includes a CPU 210 serving as acontroller of the second multifunction machine 200, a non-volatilestorage device 220 such as a hard disk drive and an EEPROM, a volatilestorage device 230 such as a RAM, a printer unit 240 configured to printan image by a predetermined method (for example, an inkjet method and alaser method), a scanner unit 250, an operation unit 260 such as a touchpanel and a button, a display unit 270 such as a liquid crystal paneloverlapped with the touch panel, and a communication unit 280 includingan interface for performing data communication with an externalapparatus such as a user's terminal apparatus and a USB memory (notshown).

The scanner unit 250 is a reading device configured to optically read adocument with an image sensor, thereby generating scan data, forexample. The scan data is RGB image data, which represents a color ofeach pixel by an RGB value including gradation values (for example, 256gradations of 0 to 255) of three color components of RGB. The gradationvalues of the three color components of RGB are also referred to as an Rvalue, a G value and a B value, respectively.

The non-volatile storage device 220 is configured to store therein acomputer program PG2. The computer program PG2 is stored in advance inthe non-volatile storage device 220 upon the manufacturing of the secondmultifunction machine 200. Instead, the computer program PG2 may bestored in a CD-ROM, a DVD-ROM and the like, or may be downloaded from aserver (not shown) connected to the second multifunction machine 200through a network. The volatile storage device 230 is provided with abuffer area 231 configured to temporarily store therein a variety ofintermediate data, which is generated when the CPU 210 performsprocessing.

The CPU 210 is configured to execute the computer program PG2, therebyimplementing a function of entirely controlling the second multifunctionmachine 200 and a function of executing plurality of types of correctionprocessing (which will be described later) for the scan data.

A model of the first multifunction machine 100 is different from a modelof the second multifunction machine 200. In this illustrativeembodiment, a manufacturer of the first multifunction machine 100 isdifferent from a manufacturer of the second multifunction machine 200.

Similarly to the second multifunction machine 200, the firstmultifunction machine 100 includes a CPU 110, a non-volatile storagedevice 120, a volatile storage device 130, a printer unit 140, a scannerunit 150, an operation unit 160, a display unit 170 and a communicationunit 180. In the non-volatile storage device 120, a computer program PG1is stored. The scanner unit 150 is a reading device configured tooptically read a document, thereby generating scan data. The scan datagenerated by the scanner unit 150 is the same RGB image data as the scandata generated by the scanner unit 250.

A-2: Correction Processing

In this illustrative embodiment, the CPU 210 of the second multifunctionmachine 200 can execute plurality of types of correction processing forthe scan data, which is a processing target. The plurality of types ofcorrection processing includes background removal processing, ghostingremoval processing, overall brightness correction processing andcontrast correction processing. The background removal processing andthe ghosting removal processing are processing of correcting abackground color in an image. Also, the overall brightness correctionprocessing and the contrast correction processing are processing ofcorrecting brightness of an image.

FIG. 2 is a flowchart and a table illustrating an outline of thecorrection processing. In S1, the CPU 210 acquires a correctionparameter corresponding to a type of the correction processing. Here,the correction parameter is a setting value relating to the correctionprocessing, which can be changed in response to a user's instruction. Asshown in the table of FIG. 2, when the correction processing to beexecuted is the background removal processing, there is no correctionparameter. When the correction processing to be executed is the ghostingremoval processing, the overall brightness correction processing or thecontrast correction processing, the correction parameter is correctionlevels L2, L3, L4, respectively. The correction levels L2, L3, L4 arerepresented by integers within a predetermined range, for example. Forinstance, the correction level L2 of the ghosting removal processing isrepresented by an integer of 0 to +20. For example, the correction levelL3 of the overall brightness correction processing and the correctionlevel L4 of the contrast correction processing are respectivelyrepresented by an integer of −10 to +10. The greater absolute value ofthe integer indicative of the correction levels L2 to L4 indicates thehigher correction level.

In S2, the CPU 210 selects one target pixel from plurality of pixels inthe scan image represented by the scan data of the processing target. InS3, the CPU 210 determines whether the target pixel is a pixel of acorrection target.

When the correction processing to be executed is the background removalprocessing, a color value (in this illustrative embodiment, the RGBvalue) in a background-approximate range RA1 approximate to a backgroundcolor of the scan image is the pixel of the processing target. In thisillustrative embodiment, the background-approximate range RA1 is a rangehaving a specific width ΔV1 with a center on a background color value(Rg, Gg, Bg), which is the RGB value indicative of the background color.The specific width ΔV1 is a fixed value. For example, in thisillustrative embodiment, the background-approximate range RA1 is a rangeof the RGB values satisfying conditions of (Rg−ΔV1)≦R≦(Rg+ΔV1),(Gg−ΔV1)≦G≦(Gg+ΔV1) and (Bg−ΔV1)≦B≦(Bg+ΔV1). As the background colorvalue (Rg, Gg, Bg), for example, a color value indicated by the mostfrequent values Rm, Gm, Bm of the R values, the G values and the Bvalues of the plurality of pixels in the scan image is used ((Rg, Gg,Bg)=(Rm, Gm, Bm)).

When the correction processing to be executed is the ghosting removalprocessing, a color value in a background-approximate range RA2approximate to the background color of the scan image is the pixel ofthe processing target. In this illustrative embodiment, thebackground-approximate range RA2 is a range having a specific width ΔV2with a center on the background color value (Rg, Gg, Bg). Therefore, thebackground-approximate range RA2 is a range similar to thebackground-approximate range RA1, for example, a range of the RGB valuessatisfying (Rg−ΔV2)≦R≦(Rg+ΔV2), (Gg−ΔV2)≦G≦(Gg+ΔV2) and(Bg−ΔV2)≦B≦(Bg+ΔV2). Meanwhile, the specific width ΔV2 of thebackground-approximate range RA2 is not a fixed value but a value to bevaried depending on the correction level L2. For example, the specificwidth ΔV2 is set to be a larger value when the correction level L2 ishigher, and is represented by ΔV2=L2×K (K: a predetermined coefficient),for example.

When the correction processing to be executed is the overall brightnesscorrection processing or the contrast correction processing, all pixelsin the scan image are pixels of the processing target.

In S3 of FIG. 2, when the target pixel is the pixel of the correctiontarget (S3: YES), in S4, the CPU 210 changes a color value (Rt, Gt, Bt)of the target pixel. When the target pixel is not the pixel of thecorrection target (S3: NO), the color value (Rt, Gt, Bt) of the targetpixel is not changed.

When the correction processing to be executed is the background removalprocessing, in S4, the color value (Rt, Gt, Bt) of the target pixel ischanged to a white value (Rwh, Gwh, Bwh), which is a specific colorvalue indicative of a specific color different from the backgroundcolor, in this illustrative embodiment, white (for example, (Rwh, Gwh,Bwh)=(255, 255, 255)).

When the correction processing to be executed is the ghosting removalprocessing, in S4, the color value (Rt, Gt, Bt) of the target pixel ischanged to a representative color of the background based on the valuesof the pixels in the background, in this illustrative embodiment, themost frequent value (Rm, Gm, Bm) used as the background color value (Rg,Gg, Bg).

When the correction processing to be executed is the overall brightnesscorrection processing, in S4, the color value (Rt, Gt, Bt) of the targetpixel is changed to a color value (Ra3, Ga3, Ba3) in accordance with atone curve C3 for overall brightness correction, for example. That is,the color value (Ra3, Ga3, Ba3) after the conversion is a value that isto be obtained by converting the color value (Rt, Gt, Bt) of the targetpixel for each component value by using the tone curve C3.

When the correction processing to be executed is the contrast correctionprocessing, in S4, the color value (Rt, Gt, Bt) of the target pixel ischanged to a color value (Ra4, Ga4, Ba4) in accordance with a tone curveC4 for overall brightness correction, for example. That is, the colorvalue (Ra4, Ga4, Ba4) after the conversion is a value that is to beobtained by converting the color value (Rt, Gt, Bt) of the target pixelfor each component value by using the tone curve C4.

FIG. 3 illustrates an example of the tone curve. In FIG. 3A, as anexample of the tone curve C3 for overall brightness correction, threetone curves C3(3), C3(6), C3(−3) of which the correction levels L3 aredifferent are shown. In FIG. 3B, as an example of the tone curve C4 forcontrast correction, three tone curves C4(3), C4(6), C4(−3) of which thecorrection levels L4 are different are shown. In the meantime, thenumbers in the parentheses of the tone curves are indicative of thecorrection levels L3, L4. Each of the curves defines a correspondencerelation between an input gradation value Vin and an output gradationvalue Vout in a coordinate system in which a horizontal axis is an inputgradation value Vin and a vertical axis is an output gradation valueVout. The tone curves are expressed by following equations (1) to (3).

Vout=(Vc/Va)×Vin(0≦Vin<Va)  (1)

Vout=(Vin−Va)×{(Vd−Vc)/(Vb−Va)}+Vc(Va≦Vin<Vb)  (2)

Vout=(Vin−Vb)×{(Vm−Vd)/(Vm−Vb)}+Vd(Vb≦Vin≦Vm)  (3)

Here, Vm indicates a maximum value of the gradation value, and in thisillustrative embodiment, Vm=255. Va to Vd are parameters defining ashape of the tone curve. In FIG. 3A, the parameters Va to Vd definingthe tone curve C3(3) of the overall brightness correction are shown, andin FIG. 3B, the parameters Va to Vd defining the tone curve C4(3) of thecontrast correction are shown.

The parameters Va, Vc set a position of a first point Pac of the tonecurve, and the parameters Vb, Vd set a position of a second point Pbd ofthe tone curve. In this illustrative embodiment, the tone curve is aline straightly connecting a start point P0 (0, 0), the first point Pac(Va, Vc), the second point Pbd (Vb, Vd) and an end point Pm (Vm, Vm).

In the overall brightness correction processing, as shown for the tonecurve C3(3) in FIG. 3A, both the first point Pac and the second pointPbd are positioned at the same side, as seen from a non-conversion line(a line LN connecting the start point P0 and the end point Pm in FIG.3). Also, a line Lm connecting the first point Pac and the second pointPbd is parallel with the non-conversion line.

In the overall brightness correction processing, when the correctionlevel L3 is a positive value, the correction of increasing the overallbrightness of an image is performed. When the correction level L3 is anegative value, the correction of decreasing the overall brightness ofan image is performed. Accordingly, when the correction level L3 is apositive value, like the tone curves C3(3), C3(6), the first point Pacand the second point Pbd are arranged above the non-conversion line LN,so that the tone curve C3 has an upwardly convex shape as a whole. Whenthe correction level L3 is a negative value, like the tone curve C3(−3),the first point Pac and the second point Pbd are arranged below thenon-conversion line LN, so that the tone curve C3 has a downwardlyconvex shape as a whole.

Also, the first point Pac and the second point Pbd are set so that thehigher the correction level L3, i.e., the greater the absolute value ofthe correction level L3, a correction amount ΔV3 becomes greater. Forexample, the correction amount ΔV3(6) of the tone curve C3(6) of FIG. 3Ais about twice as great as the correction amounts ΔV3(3), ΔV3(−3) of thetone curves C3(3), C3(−3), respectively.

In the contrast correction processing, as shown for the tone curve C4(3)of FIG. 3B, the first point Pac is positioned at one side, with respectto the non-conversion line LN, and the second point Pbd is positioned atthe other side, with respect to the non-conversion line LN. The line Lmconnecting the first point Pac and the second point Pbd intersects witha central point of the non-conversion line LN.

In the contrast correction processing, when the correction level L4 is apositive value, the correction of increasing the contrast of an image,i.e., the correction of increasing a difference of the brightnessbetween a relatively dark area and a relatively bright area in an imageis performed. Also, when the correction level L4 is a negative value,the correction of decreasing the contrast of an image, i.e., thecorrection of decreasing a difference of the brightness between arelatively dark area and a relatively bright area in an image isperformed. Accordingly, when the correction level L4 is a positivevalue, like the tone curves C4(3), C4(6), the first point Pac positionedin a low gradation-side area LA is arranged below the non-conversionline LN, and the second point Pbd positioned in a high gradation-sidearea HA is arranged above the non-conversion line LN. When thecorrection level L4 is a negative value, like the tone curve C4(−3), thefirst point Pac positioned in the low gradation-side area LA is arrangedabove the non-conversion line LN, and the second point Pbd positioned inthe high gradation-side area HA is arranged below the non-conversionline LN. The high gradation-side area HA is a range (for example, 128 to255) of values of an intermediate value (for example, 128) or greater ofa range (for example, 0 to 255) of the gradation values. The lowgradation-side area LA is a range (for example, 0 to 128) of valuessmaller than the intermediate value of the range of the gradationvalues.

Also, the first point Pac and the second point Pbd are set so that thehigher the correction level L4, i.e., the greater the absolute value ofthe correction level L4, the correction amount ΔV4 becomes greater. Forexample, the correction amount ΔV4(6) of the tone curve C4(6) of FIG. 3Bis about twice as great as the correction amounts ΔV4(3), ΔV4(−3) of thetone curves C4(3), C4(−3).

In S5 of FIG. 2, the CPU 210 determines whether all the pixels in theimage of the processing target have been processed as the target pixel.When there is a non-processed pixel (S5: NO), the CPU 410 returns to S2and selects the non-processed pixel as the target pixel. When all thepixels have been processed (S5: YES), the CPU 210 terminates thecorrection processing.

In the meantime, the CPU 110 of the first multifunction machine 100 ofthis illustrative embodiment can execute four types of correctionprocessing, which correspond to the four types of correction processingthat is executable by the second multifunction machine 200, for the scandata. Each of the four types of correction processing that is executableby the CPU 110 of the first multifunction machine 100 is processingcapable of accomplishing the equivalent result to the correspondingprocessing of the four types of correction processing that is executableby the second multifunction machine 200, and is expressed by the samename (for example, the background removal processing, the ghostingremoval processing and the like), in the below.

A-3. Operations of Second Multifunction Machine 200

In this illustrative embodiment, a case is assumed in which a user ofthe first multifunction machine 100 switches the multifunction machinebeing used from the first multifunction machine 100 to the secondmultifunction machine 200. Currently, the user enables the firstmultifunction machine 100 to generate the scan data and also enables thefirst multifunction machine 100 to execute the correction processing forthe scan data.

The second multifunction machine 200 can execute correction informationregistration processing for reducing a user's trouble on the setting ofthe second multifunction machine 200. The correction informationregistration processing is to support the user's setting on the secondmultifunction machine 200 so that the correction processing capable ofaccomplishing the equivalent result to the correction processing, whichis to be executed by the CPU 110 of the first multifunction machine 100,can be executed by the second multifunction machine 200.

A-3-1. Preparation of Image Data for Analysis and the Like

Before executing the correction information registration processing bythe second multifunction machine 200, the user prepares, as image datafor analysis, original image data and corrected image data.

FIG. 4 illustrates an example of the original image and the correctedimage. An original image IA of FIG. 4A is an image represented by theoriginal image data. The original image data is RGB image data generatedby optically reading a document OP1 with the scanner unit 150 of thefirst multifunction machine 100. FIG. 4A illustrates not only theoriginal image IA but also the document OP1.

The document OP1 is a document where an image including an object Ob1including characters Tx1 and an object Ob2 including figures is printedon a sheet having a background color (for example, light red or blue)different from white, for example. Therefore, the original image IAincludes a background GA having the background color of the sheet atparts except for the objects Ob1, Ob2. In the meantime, the backgroundGA includes a ghosting OF. The ghosting OF is an image that is includedin the original image IA as an image of a backside of the document OP1is unintentionally read when reading a surface of the document OP1 withthe scanner unit 150. In general, the ghosting OF is an image having acolor approximate to the color of the background GA.

The corrected image data is generated when the CPU 110 of the firstmultifunction machine 100 executes specific correction processing forthe original image data. For example, when the user wants the secondmultifunction machine 200 to execute the correction processing, which isequivalent to the specific correction processing being executed by thefirst multifunction machine 100, the user prepares the corrected imagedata generated as the specific correction processing is executed for theoriginal image data. As a corrected image IB expressed by the correctedimage data, corrected images IB1 to IB4 to be described later may beexemplified.

For example, first corrected image data, which is generated as thebackground removal processing is executed for the original image data,may be prepared. The first corrected image IB1 of FIG. 4B expressed bythe first corrected image data includes the objects Ob1, Ob2 and abackground GB1 of which color has been changed to white. In thebackground GB1 of which color has been changed to white, the ghosting OFis not included. The reason is that since the ghosting OF has a colorapproximate to the color of the background GA, the colors of theplurality of pixels configuring the ghosting OF have been changed to thewhite value by the background removal processing.

Also, second corrected image data, which is generated as the ghostingremoval processing is executed for the original image data, may beprepared. The second corrected image IB2 of FIG. 4C expressed by thesecond corrected image data includes the objects Ob1, Ob2 and abackground GB2. The background GB2 has the same color as the backgroundGA of the original image IA but does not include the ghosting OF. Thereason is that the colors of the plurality of pixels configuring theghosting OF have been changed to the background color value by theghosting removal processing.

Also, third corrected image data, which is generated as the overallbrightness correction processing is executed for the original imagedata, and fourth corrected image data, which is generated as thecontrast correction processing is executed for the original image data,may be prepared. The third corrected image IB3 expressed by the thirdcorrected image data and the fourth corrected image IB4 expressed by thefourth corrected image data are omitted.

For example, the user stores the original image data and the correctedimage data, which was previously generated by the first multifunctionmachine 100, from the non-volatile storage device 120 of the firstmultifunction machine 100 or a terminal such as a PC and a smart phoneinto a removable memory such as a USB memory.

In this way, the original image data and the corrected image data aregenerated by the first multifunction machine 100 different from thesecond multifunction machine 200 serving as the image processingapparatus of this illustrative embodiment.

A-3-2. Correction Information Registration Processing

In the correction information registration processing, the originalimage data and the corrected image data are analyzed to perform thesetting on the second multifunction machine 200 so that the specificcorrection processing being executed for the original image data can beexecuted by the second multifunction machine 200. The correctioninformation registration processing is executed by the CPU 210 of thesecond multifunction machine 200 when an execution instruction from theuser is input through the operation unit 260, for example. For instance,the user inputs an execution instruction at a state where the removablememory, in which the original image data and the corrected image dataare stored, is connected to the communication unit 280 of the secondmultifunction machine 200.

FIG. 5 is a flowchart of the correction information registrationprocessing. In S10, the CPU 210 acquires the original image data fromthe removable memory connected to the communication unit 280 and storesthe same in the buffer area 231. In S20, the CPU 210 acquires thecorrected image data from the removable memory and stores the same inthe buffer area 231.

In S30, the CPU 210 executes correction type presumption processing byusing the original image data and the corrected image data. In thecorrection type presumption processing, a first index value relating toa color distribution of the original image IA and a second index valuerelating to a color distribution of the corrected image IB arecalculated, and processing corresponding to the specific correctionprocessing executed for the original image data is presumed from pluraltypes of candidates for processing, based on comparison of the indexvalues. In this illustrative embodiment, plural types of candidates forprocessing is the four types of correction processing that is executableby the second multifunction machine 200. The correction type presumptionprocessing will be described in detail later.

In S40, the CPU 210 executes correction level determination processingby using the original image data and the corrected image data. In thecorrection level determination processing, the correction levels L2 toL4, which are the correction parameters for the correspondenceprocessing presumed in the correction type presumption processing, aredetermined. As described in detail later, the correction level isdetermined using a method corresponding to the presumed correspondenceprocessing. However, when the presumed correspondence processing is thebackground removal processing, the correction level determinationprocessing is not performed because there is no correction level, asdescribed above.

In S50, the CPU 210 displays a result notification screen WI1, which isa user interface (UI) screen for notifying the user of a presumptionresult, on the display unit 270. FIG. 6 illustrates an example of theresult notification screen WI1. The result notification screen WI1includes a text AN indicative of the presumed correspondence processing,a slide bar SB indicating the determined correction level, and a fieldF1 for inputting a registration name of correction information.

In this illustrative embodiment, the text AN indicates any one of thebackground removal processing, the ghosting removal processing, theoverall brightness correction processing and the contrast correctionprocessing.

The slide bar SB is a UI element for designating the correction level.The slide bar SB includes a bar BR and a slider SD configured to movealong the bar BR in accordance with an operation of the user. The sliderSD may be moved to any one of predetermined number (for example, 20) ofpositions along the bar BR, for example. Each of the predeterminednumber of moveable positions corresponds to each of the settablecorrection levels one-on-one. For example, in the example of FIG. 6, aposition of the predetermined number of positions, which is closer to aright end of the bar BR, corresponds to a higher correction level, and aposition closer to a left end of the bar BR corresponds to a lowercorrection level.

When the result notification screen WI1 is displayed, the slider SD isarranged at a position corresponding to a correction level determined inthe correction level determination processing. Thereby, the user isnotified of the determined correction level. The user can adjust thecorrection level by moving the slider SD to a position corresponding toa desired correction level. That is, the user can input an adjustmentinstruction of the correction level through the result notificationscreen WI1. The CPU 210 adjusts the correction level, based on theuser's adjustment instruction. As a result, it is possible to adjust theautomatically determined correction level, based on the user'sinstruction, so that it is possible to determine an appropriatecorrection level without placing an excessive trouble on the user.

In S60, the CPU 210 determines whether a registration instruction of thecorrection information is input through the result notification screenWI1. For example, when the user presses a registration button BT1, it isdetermined that a registration instruction is input, and when the userpresses a cancel button BT2, it is determined that a registrationinstruction is not input.

When a registration instruction is input (S60: YES), in S70, the CPU 210registers the correction information, which includes a correction levelcorresponding to a position of the slider SD at the time of the input ofthe registration instruction and the information indicative of one ofthe four types of correction processing, in the non-volatile storagedevice 220 in association with the registration name, and terminates thecorrection information registration processing. As the registrationname, a text input in the field F1 at the time of the input of theregistration instruction is used. When a registration instruction is notinput (S60: NO), the CPU 210 skips over S70, and terminates thecorrection information registration processing.

The correction information registration processing can be executedseveral times by changing the combination of the original image data andthe corrected image data into a combination of two other image data, forexample, in response to the execution instruction of the user. In thiscase, the correction information is determined and registered for eachof combinations of plurality of sets. For example, the plurality ofcorrection information may be registered for each of plurality of typesof documents.

According to the above-described registration processing, thecorrespondence processing to the specific correction processing ispresumed based on the comparison of the first index value relating tothe color distribution of the original image IA and the second indexvalue relating to the color distribution of the corrected image IB (S40in FIG. 5). As a result, it is possible to appropriately presume whichof plural types of candidates for processing that is executable by theimage processing apparatus corresponds to the specific correctionprocessing, which has been executed for the original image data,according to the color distribution of the original image IA and thecolor distribution of the corrected image IB, corresponds. As a result,for example, it is possible to notify the user of the presumedcorrection processing or to automatically execute the presumedcorrection processing, so that it is possible to reduce the trouble ofthe user who uses the correction processing.

More specifically, it may not be easy for the user to manually set, onthe UI screen, the correction processing to be executed at the secondmultifunction machine 200 so that the equivalent processing result tothe correction processing executed at the first multifunction machine100 can be obtained. For example, the names of the correction processingthat are to be used in the UI screen may be different depending on themultifunction machines. For example, the ghosting removal processing mayalso be referred to as ‘color clean up.’ Also, the configuration of theUI screen, the sequence of displaying the UI screen and the like aredifferent depending on the multifunction machines. In particular, like acase where the different correction processing is respectively performedfor each of the plurality of types of documents, it may be necessary toregister plurality of correction information, so that the user's troubleincreases. In this illustrative embodiment, since the processingcorresponding to the correction processing that is being executed by thefirst multifunction machine 100 is automatically presumed from theplurality of candidate processing that can be executed by the secondmultifunction machine 200, it is possible to reduce the user's troubleof making a setting (for example, registration of the correctioninformation) relating to the correction processing at the secondmultifunction machine 200.

Further, the correction level, which is the parameter for the presumedcorrespondence processing, is determined using the method correspondingto the presumed correspondence processing (S40). As a result, it ispossible to automatically determine the correction level appropriately.Therefore, it is possible to reduce the user's trouble to set thecorrection level of the correction processing at the secondmultifunction machine 200. For example, the relation between the valuesof the correction levels L2, L3, L4 and the specific width ΔV2, thecorrection amount ΔV3, ΔV4 may be different depending on themultifunction machines, for example. Accordingly, it cannot be said thatit is easy for the user to set the appropriate correction level at thesecond multifunction machine 200 so that the processing resultequivalent to the correction processing executed at the firstmultifunction machine 100 can be obtained. In particular, as describedabove, when it is necessary to register the plurality of correctioninformation, for example, the user's trouble increases. In thisillustrative embodiment, since it is possible to determine thecorrection level by using the method corresponding to the presumedcorrespondence processing, it is possible to automatically determine theappropriate correction level easily. Therefore, it is possible to reducethe user's trouble to set the correction level at the secondmultifunction machine 200.

Further, in 850, the result notification screen WI1 is displayed, sothat the presumed correspondence processing is notified to the user. Asa result, it is possible to reduce the user's trouble. Specifically, theuser can easily register the presumed correspondence processing in thesecond multifunction machine 200. Also, since the user can recognize theprocessing corresponding to the correction processing that can beexecuted by the first multifunction machine 100 previously used, it ispossible to easily enrich the understanding about the secondmultifunction machine 200 without referring to the manual and the like.

Also, since the presumed correction level is notified to the user on theresult notification screen WI1, the user's trouble is further reduced.Specifically, the user can easily register the correction informationindicative of the presumed correspondence processing and the correctionlevel in the second multifunction machine 200. Also, since the user canrecognize the correction level corresponding to the correction level setat the first multifunction machine 100 previously used, it is possibleto easily enrich the understanding about the second multifunctionmachine 200.

A-3-3. Correction Type Presumption Processing

The correction type presumption processing of S30 in FIG. 5 will bedescribed. FIG. 7 is a flowchart of the correction type presumptionprocessing. In S100, a histogram indicative of a color distribution ofthe original image IA is generated by using the original image data. Thehistogram is data obtained by classifying the respective pixels of theoriginal image data into plurality of classes according to the RGBvalues of the pixels. In this illustrative embodiment, each of thegradation values of 256 gradations is set to one class and the histogramis generated for each of the three color components of the RGB value.

FIG. 8 illustrates examples of the histogram of the original image IAand the distribution area of the original image IA. In FIG. 8A, thehistogram of the original image IA is shown for each of the three colorcomponents of RGB. The histograms are simplified for illustration.

The three histograms include peaks PBr, PBg, PBb corresponding to thebackground GA and peaks POr, POg, POb corresponding to the objects Ob1,Ob2 in the original image IA of FIG. 4A, respectively. The gradationvalue at a position of the peak is also referred to as a peak gradationvalue.

In S105, the CPU 210 calculates the first index value relating to thecolor distribution of the original image IA. In this illustrativeembodiment, as the first index value, for each of the three colorcomponents, the most frequent value Rm, Gm, Bm, number of pixels (alsoreferred to as number of most frequent pixels) RmN, GmN, BmN having themost frequent value Rm, Gm, Bm, an opposite-side most frequent value Ro,Go, Bo, and a width Rw, Gw, Bw of the distribution area are calculated.It can be said that number of most frequent pixels RmN, GmN, BmN is apeak height corresponding to the most frequent value. The opposite-sidemost frequent value Ro of the R component is the most frequent value inan area of the high gradation-side area HA and the low gradation-sidearea LA, which is opposite to an area in which the most frequent valueRm of the R component exists. The opposite-side most frequent values Go,Bo of the G and B components are also the same.

As shown in FIG. 8A, in a general image, the most frequent values Rm,Gm, Bm are the peak gradation values of the peaks PBr, PBg, PBbcorresponding to the background GA. The reason is that since thebackground GA is usually more uniform and wider, as compared to theobjects Ob1, Ob2, the peaks PBr, PBg, PBb corresponding to thebackground GA become the highest peaks in the histograms of therespective color components. The opposite-side most frequent values Ro,Go, Bo are the peak gradation values of the peaks POr, POg, PObcorresponding to the objects Ob1, Ob2, for example.

The width Rw of the distribution area of the R component is a width of arange of the gradation values of the pixels, which are equal to orgreater than a reference number THn, of the 256 gradation values of theR component. The reference number THn corresponds to 3% of a totalnumber of pixels in an image, for example. In FIG. 8A, the referencenumber THn is indicated by a horizontal dotted line. In FIG. 8B, thedistribution areas RDo, RDb, GDo, GDb, BDo, BDb of the gradation valueshas number of the pixels equal to or greater than the reference numberTHn. In the example of FIG. 8B, the width Rw of the distribution area ofthe R component is a sum value of the widths Rw1, Rw2 of the twodistribution areas RDo, RDb (Rw=Rw1+Rw2). The widths Gw, Bw of thedistribution areas of the G and B components are also the same.

In S110 of FIG. 7, a histogram indicative of a color distribution of thecorrected image IB is generated by using the corrected image data.Similarly to the histograms of the original image IA of FIG. 8, ahistogram is generated for each of the three color components of the RGBvalue.

FIGS. 9 and 10 illustrate examples of the histogram of the correctedimage IB. FIG. 11 illustrates examples of the distribution area of thecorrected image IB. In FIGS. 9A and 9B, the histograms of the firstcorrected image IB1, which are generated by using the first correctedimage data, and the histograms of the second corrected image IB2, whichare generated by using the second corrected image data, are shown. InFIGS. 10A and 10B, the histograms of the third corrected image IB3,which are generated by using the third corrected image data, and thehistograms of the fourth corrected image IB4, which are generated byusing the fourth corrected image data, are shown. Also, in FIG. 11, thedistribution areas of the corrected images IB1 to IB4 are respectivelyshown. The histogram and the distribution area are different dependingon which the corrected image data the corrected image data of theprocessing target corresponds to, i.e., depending on which of thebackground removal processing, the ghosting removal processing, theoverall brightness correction processing and the contrast correctionprocessing the specific correction processing is executed for theoriginal image data.

The generated histogram of each color component includes the peakcorresponding to the background and the peak corresponding to theobjects, like the histogram in the original image IA of FIG. 4A. Forexample, the three histograms of the first corrected image IB1 shown inFIG. 9A include the peaks PBr1, PBg1, PBb1 corresponding to thebackground GB1 (FIG. 4B) and the peaks POr1, POg1, POb1 corresponding tothe objects Ob1, Ob2, respectively. Meanwhile, in the histograms ofFIGS. 9 and 10, the peak indicated by the dotted line is the peak of thehistogram of the original image IA shown in FIG. 8A, and is shown forcomparison with the peak of the histogram of the corrected image IB.

In S115, the CPU 210 calculates the second index value relating to thecolor distribution of the corrected image IB. In this illustrativeembodiment, as the second index value, for each of the three colorcomponents, the most frequent value, number of most frequent pixels, theopposite-side most frequent value, and the width of the distributionarea are calculated, like the first index value. For example, when thecorrected image data of the processing target is the first image data,as the second index value, the most frequent values Rm1, Gm1, Bm1, thenumbers of most frequent pixels RmN1, GmN1, BmN1, the opposite-side mostfrequent values Ro1, Go1, Bo1, and the widths Rw1, Gw1, Bw1 of thedistribution area are calculated (FIG. 9A, FIG. 11A). Meanwhile, in FIG.11, the distribution area indicated by the dotted line is thedistribution area of the original image IA shown in FIG. 8A, and isshown for comparison with the distribution area of the corrected imageIB.

In S120 of FIG. 7, the CPU 210 executes the background removalpresumption processing of presuming whether the correspondenceprocessing to the specific correction processing executed for theoriginal image data is the background removal processing by using thecalculated first index value and second index value. In S125, the CPU210 determines whether the correspondence processing is presumed as thebackground removal processing, based on a result of the backgroundremoval presumption processing. When the correspondence processing ispresumed as the background removal processing (S125: YES), thecorrection type presumption processing is terminated, and when thecorrespondence processing is presumed not as the background removalprocessing (S125: NO), the CPU 210 proceeds to S130.

In S130, the CPU 210 executes the ghosting removal presumptionprocessing of presuming whether the correspondence processing to thespecific correction processing is the ghosting removal processing byusing the calculated first index value and second index value. In S135,the CPU 210 determines whether the correspondence processing is presumedas the ghosting removal processing, based on a result of the ghostingremoval presumption processing. When the correspondence processing ispresumed as the ghosting removal processing (S135: YES), the correctiontype presumption processing is terminated, and when the correspondenceprocessing is presumed not as the ghosting removal processing (S135:NO), the CPU 210 proceeds to S140.

In S140, the CPU 210 executes the overall brightness correctionpresumption processing of presuming whether the correspondenceprocessing to the specific correction processing is the overallbrightness correction processing by using the calculated first indexvalue and second index value. In S145, the CPU 210 determines whetherthe correspondence processing is presumed as the overall brightnesscorrection processing, based on a result of the overall brightnesscorrection presumption processing. When the correspondence processing ispresumed as the overall brightness correction processing (S145: YES),the correction type presumption processing is terminated, and when thecorrespondence processing is presumed not as the overall brightnesscorrection processing (S145: NO), the CPU 210 proceeds to S150.

In S150, the CPU 210 executes the contrast correction presumptionprocessing of presuming whether the correspondence processing to thespecific correction processing is the contrast correction processing byusing the calculated first index value and second index value. In S155,the CPU 210 determines whether the correspondence processing is presumedas the contrast correction processing, based on a result of the contrastcorrection presumption processing. When the correspondence processing ispresumed as the contrast correction processing (S155: YES), thecorrection type presumption processing is terminated, and when thecorrespondence processing is presumed not as the contrast correctionprocessing (S155: NO), the CPU 210 determines that the correspondenceprocessing cannot be presumed, and terminates the correction typepresumption processing, in S150.

According to the above-described correction type presumption processing,the histograms of the original image IA are generated by using theoriginal image data (S100), and the first index value is calculated byusing the histograms (S105). The histograms of the corrected image IBare generated by using the corrected image data (S110), and the secondindex value is calculated by using the histograms (S115). The processingof presuming the correspondence processing is performed using the firstindex value and the second index value (S120, S130, S140, S150). As aresult, since it is possible to compare the two images with highprecision by using the histograms of the original image IA and thehistograms of the corrected image IB, it is possible to appropriatelypresume the correspondence processing.

A-3-4. Background Removal Presumption Processing

The background removal presumption processing of S120 in FIG. 7 will bedescribed. FIG. 12 is a flowchart of the background removal presumptionprocessing. In S210, the CPU 210 determines whether the color indicatedby the most frequent values of the original image IA is white.Specifically, it is determined whether the RGB value (Rm, Gm, Bm)including the most frequent values Rm, Gm, Bm is the white value (255,255, 255). Instead, the color value for which it is determined as whitemay be provided with a width and it may be determined whether the RGBvalue (Rm, Gm, Bm) is within the range indicative of the white, forexample, the range of (250 to 255, 250 to 255, 250 to 255). As describedabove, the RGB value (Rm, Gm, Bm) including the most frequent values Rm,Gm, Bm is considered as the background color value indicative of thecolor of the background GA of the original image IA. Therefore, it canbe said that it is determined in S210 whether the color of thebackground GA of the original image IA is white. For example, in theexample of FIG. 4A, since the color of the background GA of the originalimage IA is not white, the RGB value (Rm, Gm, Bm) is not the white value(255, 255, 255), as shown in FIG. 8A. In this case, therefore, it isdetermined in S210 that the color indicated by the most frequent valuesRm, Gm, Bm of the original image IA is not white.

When it is determined that the color indicated by the most frequentvalues Rm, Gm, Bm of the original image IA is not white (S210: NO), theCPU 210 determines whether the color indicated by the most frequentvalues of the corrected image IB is white, in S220. For example, whenthe first corrected image data is the processing target, since the RGBvalue (Rm1, Gm1, Bm1) including the most frequent values Rm1, Gm1, Bm1is the white value (255, 255, 255), as shown in FIG. 9A, it isdetermined that the color indicated by the most frequent values of thefirst corrected image IB1 is white. On the other hand, when the secondcorrected image data is the processing target, since the RGB value (Rm2,Gm2, Bm2) including the most frequent values Rm2, Gm2, Bm2 is not thewhite value (255, 255, 255), as shown in FIG. 9B, it is determined thatthe color indicated by the most frequent values of the second correctedimage IB2 is not white. It can be said that it is determined in S220whether the background color of the corrected image IB is white.

When the color indicated by the most frequent values of the correctedimage IB is white (S220: YES), the CPU 210 presumes in S230 that thecorrespondence processing is the background removal processing.

When the color indicated by the most frequent values Rm, Gm, Bm of theoriginal image IA is white (S210: YES), or when the color indicated bythe most frequent values of the corrected image IB is not white (S220:NO), the CPU 210 presumes in S240 that the correspondence processing isnot the background removal processing.

According to the above-described background removal presumptionprocessing of this illustrative embodiment, when the background colorvalue of the original image IA, specifically, the color value includingthe most frequent values indicates the color different from the specificcolor (white, in this illustrative embodiment) (S210: NO) and thebackground color value of the corrected image IB indicates the specificcolor (S220: YES), the correspondence processing is presumed as thebackground removal processing. As a result, it is possible toappropriately presume whether the correspondence processing is thebackground removal processing by using the most frequent value, which isthe representative value of the image indicating the background color.For example, in the background removal processing, even when thebackground of the original image IA has a color different from white,the background of the corrected image IB becomes white. In contrast, inthe ghosting removal processing, the contrast correction processing andthe overall brightness correction processing, when the background of theoriginal image IA has a color different from white, the background ofthe corrected image IB is not white. In this way, by the backgroundremoval presumption processing, it is possible to precisely presumewhether the correspondence processing is the background removalprocessing.

A-3-5. Ghosting Removal Presumption Processing

The ghosting removal presumption processing of S130 in FIG. 7 will bedescribed. FIG. 13 is a flowchart of the ghosting removal presumptionprocessing. In S310, the CPU 210 determines whether the most frequentvalue of the corrected image IB is shifted with respect to the mostfrequent value of the original image IA.

In this illustrative embodiment, when a first value V1 and a secondvalue V2 satisfy a shift condition, the CPU 210 determines that thesecond value V2 is shifted with respect to the first value V1, and whenthe first value V1 and the second value V2 do not satisfy a shiftcondition, the CPU 210 determines that the second value V2 is notshifted with respect to the first value V1. The shift condition is thatan absolute value |V1−V2| of a difference between the first value V1 andthe second value V2 is greater than a threshold THs for shiftdetermination (|V1−V2|>THs). The threshold THs for shift determinationis ‘5’, for example, when the range of the values, which can be taken asthe first value V1 and the second value V2, is 0 to 255.

In S310, when the most frequent value of the original image IA and themost frequent value of the corrected image IB satisfy the shiftcondition for each of the three color components, it is determined thatthe most frequent value of the corrected image IB is shifted withrespect to the most frequent value of the original image IA. When themost frequent value of the original image IA and the most frequent valueof the corrected image IB do not satisfy the shift condition for any oneof the three color components, it is determined that the most frequentvalue of the corrected image IB is not shifted with respect to the mostfrequent value of the original image IA.

For example, when the second corrected image data is the processingtarget, it is determined that the most frequent values Rm2, Gm2, Bm2 ofthe second corrected image IB2 are not shifted, as can be seen from FIG.9B. On the other hand, when the first corrected image data is theprocessing target, it is determined that the most frequent values Rm1,Gm1, Bm1 of the first corrected image IB1 are shifted, as can be seenfrom FIG. 9A.

When it is determined that the most frequent value of the correctedimage IB is not shifted with respect to the most frequent value of theoriginal image IA (S310: NO), the CPU 210 determines whether number ofmost frequent pixels of the corrected image IB is increased with respectto number of most frequent pixels of the original image IA, in S320.Specifically, when a difference obtained by subtracting number of mostfrequent pixels of the original image IA from number of most frequentpixels of the corrected image IB is equal to or greater than a thresholdTHi for each of the three color components, it is determined that numberof most frequent pixels of the corrected image IB is increased withrespect to number of most frequent pixels of the original image IA. Whenthe difference obtained by subtracting number of most frequent pixels ofthe original image IA from number of most frequent pixels of thecorrected image IB is smaller than the threshold THi for any one of thethree color components, it is determined that number of most frequentpixels of the corrected image IB is not increased with respect to numberof most frequent pixels of the original image IA. The threshold THi is avalue equal to or twice as great as number of most frequent pixels ofthe original image IA, for example. Accordingly, when number of mostfrequent pixels of the corrected image IB is remarkably increased withrespect to number of most frequent pixels of the original image IA bytwo or three times or greater, it is determined in S320 that number ofmost frequent pixels of the corrected image IB is increased.

For example, when the second corrected image data is the processingtarget, the peak heights corresponding to the most frequent values arehigher than the original image data for all the color components, as canbe seen from FIG. 9B. Accordingly, in this case, it is determined thatnumber of most frequent pixels of the corrected image IB is increasedwith respect to number of most frequent pixels of the original image IA.

When it is determined that number of most frequent pixels of thecorrected image IB is increased with respect to number of most frequentpixels of the original image IA (S320: YES), the CPU 210 determineswhether the width of the distribution area of the corrected image IB isreduced with respect to the width of the distribution area of theoriginal image IA, in S330.

In this illustrative embodiment, when a width W1 of a first distributionarea and a width W2 of a second distribution area satisfy a reductioncondition for one color component, the CPU 210 determines that the widthW2 of the second distribution area is reduced with respect to the widthW1 of the first distribution area for the color component, and the widthW1 of the first distribution area and the width W2 of the seconddistribution area do not satisfy a reduction condition, the CPU 210determines that the width W2 of the second distribution area is notreduced with respect to the width W1 of the first distribution area. Thereduction condition is that a difference (W1−W2) obtained by subtractingthe width W2 of the second distribution area from the width W1 of thefirst distribution area is greater than a threshold Thw for widthdetermination ((W1−W2)>THw). The threshold Thw for width determinationis ‘5’, for example, when the range of the values, which can be taken asthe gradation value of each component of RGB, is 0 to 255.

In S330, when the width of the distribution area is reduced for each ofthe three color components, it is determined that the width of thedistribution area of the corrected image IB is reduced with respect tothe width of the distribution area of the original image IA. When thewidth of the distribution area is not reduced for any one of the threecolor components, it is determined that the width of the distributionarea of the corrected image IB is not reduced with respect to the widthof the distribution area of the original image IA.

For example, when the second corrected image data is the processingtarget, the widths Rwb2, Gwb2, Bwb2 of the distribution areascorresponding to the background of the second corrected image IB2 areconsiderably reduced with respect to the widths Rwb, Gwb, Bwb of thedistribution areas corresponding to the background of the original imageIA, as can be seen from FIGS. 8B and 11B. Accordingly, in this case, itis determined in S330 that the width of the distribution area of thecorrected image IB is reduced with respect to the width of thedistribution area of the original image IA.

When the width of the distribution area of the corrected image IB isreduced with respect to the width of the distribution area of theoriginal image IA (S330: YES), the CPU 210 presumes in S340 that thecorrespondence processing is the ghosting removal processing.

When the most frequent value of the corrected image IB is shifted withrespect to the most frequent value of the original image IA (S310: YES),when number of most frequent pixels of the corrected image IB is notincreased with respect to number of most frequent pixels of the originalimage IA (S320: NO), or when the width of the distribution area of thecorrected image IB is not reduced with respect to the width of thedistribution area of the original image IA (S330: NO), the CPU 210presumes in S350 that the correspondence processing is not the ghostingremoval processing.

According to the above-described ghosting removal presumption processingof this illustrative embodiment, when the background color value of thecorrected image IB is not shifted with respect to the background colorvalue (specifically, the color value including the most frequent value)of the original image IA (S310: NO), number of pixels having thebackground color of the corrected image IB is increased with respect tonumber of pixels (specifically, number of most frequent pixels) havingthe background color of the original image IA (S320: YES) and the widthof the distribution area of the corrected image IB is reduced withrespect to the width of the distribution area of the original image IA(S330: YES), the correspondence processing is presumed as the ghostingremoval processing. As a result, it is possible to appropriately presumewhether the correspondence processing is the background removalprocessing by using the most frequent value, which is the representativevalue of the image indicative of the background color, number of mostfrequent pixels and the width of the distribution area. For example,while the background color value is shifted in the background removalprocessing, the contrast correction processing and the overallbrightness correction processing (FIGS. 9A, 10A, 10B), the backgroundcolor value is not shifted in the ghosting removal processing (FIG. 9B).Also, while number of most frequent pixels is not remarkably increasedin the contrast correction processing and the overall brightnesscorrection processing, number of most frequent pixels is considerablyincreased in the ghosting removal processing. Also, while the width ofthe distribution area is little changed in the overall brightnesscorrection processing, the width of the distribution area is reduced inthe background removal processing. In this way, it is possible toprecisely presume whether the correspondence processing is the ghostingremoval processing by the ghosting removal presumption processing.

Also, as can be seen from the descriptions of the background removalpresumption processing and the ghosting removal presumption processing,in this illustrative embodiment, the correspondence processing ispresumed by using, the respective most frequent values of the originalimage IA and the corrected image IB. It is considered that the mostfrequent value is indicative of the background color in the image.Accordingly, according to this illustrative embodiment, it is possibleto appropriately presume the correspondence processing from plural typesof candidates for processing (for example, the background removalprocessing and the ghosting removal processing) of correcting thebackground color.

A-3-6. Overall Brightness Correction Presumption Processing

The overall brightness correction presumption processing of S140 in FIG.7 will be described. FIG. 14 is a flowchart of the overall brightnesscorrection presumption processing. In S410, the CPU 210 determineswhether the distribution area of the corrected image IB is shifted withrespect to the distribution area of the original image IA. For example,when the most frequent value of the corrected image IB is shifted withrespect to the most frequent value of the original image IA and theopposite-side most frequent value of the corrected image IB is shiftedwith respect to the opposite-side most frequent value of the originalimage IA, it is determined that the distribution area of the correctedimage IB is shifted with respect to the distribution area of theoriginal image IA. At this time, when the most frequent values Rm, Gm,Bm of the original image IA are the maximum value (255, in thisillustrative embodiment) of the range of values that can be taken as thegradation value, since the most frequent values are not shifted in thepositive direction, if the opposite-side most frequent values areshifted, it may be determined that the distribution area of thecorrected image IB is shifted.

For example, when the third corrected image data is the processingtarget, as can be seen from FIG. 11C, the corresponding distributionareas RDo3, RDb3, GDo3, GDb3, BDo3, BDb3 of the third corrected imageIB3 are shifted with respect to the respective distribution areas RDo,RDb, GDo, GDb, BDo, BDb of the original image IA. Accordingly, as can beseen from FIG. 10A, the most frequent values Rm3, Gm3, Bm3 of the thirdcorrected image IB3 are shifted with respect to the most frequent valuesRm, Gm, Bm of the original image IA and the opposite-side most frequentvalues Ro3, Go3, Bo3 of the third corrected image IB3 are also shiftedwith respect to the opposite-side most frequent values Ro, Go, Bo of theoriginal image IA. Therefore, when the third corrected image data is theprocessing target, it is determined that the distribution area of thecorrected image IB is shifted.

For example, when the second corrected image data is the processingtarget, as can be seen from FIG. 11B, the corresponding distributionareas RDo2, GDo2, BDo2 of the second corrected image IB2 are not shiftedwith respect to the distribution areas RDo, GDo, BDo corresponding tothe object of the original image IA. Accordingly, when the secondcorrected image data is the processing target, it is determined that thedistribution area of the corrected image IB is not shifted.

When it is determined that the distribution area of the corrected imageIB is shifted with respect to the distribution area of the originalimage IA (410: YES), the CPU 210 determines whether the width of thedistribution area of the corrected image IB is changed with respect tothe width of the distribution area of the original image IA, in S420.

In this illustrative embodiment, when the width W1 of the firstdistribution area and the width W2 of the second distribution areasatisfy a change condition for one color component, the CPU 210determines that the width W2 of the second distribution area is changedwith respect to width W1 of the first distribution area for the colorcomponent, and when the width W1 of the first distribution area and thewidth W2 of the second distribution area do not satisfy a changecondition, the CPU 210 determines that the width W2 of the seconddistribution area is not changed with respect to width W1 of the firstdistribution area. The change condition is that an absolute value|W1−W2| of a difference between the width W1 of the first distributionarea and the width W2 of the second distribution area is smaller thanthe threshold THw for width determination (|W1−W2|<THw).

In S420, when the width of the distribution area is not changed for eachof the three color components, it is determined that the width of thedistribution area of the corrected image IB is not changed with respectto the width of the distribution area of the original image IA. When thewidth of the distribution area is changed for any one of the three colorcomponents, it is determined that the width of the distribution area ofthe corrected image IB is changed with respect to the width of thedistribution area of the original image IA.

For example, when the third corrected image data is the processingtarget, as can be seen from FIGS. 8B and 11C, the widths Rw3, Gw3, Bw3of the distribution area corresponding to the background of the thirdcorrected image IB3 are not changed with respect to the widths Rw, Gw,Bw of the distribution area of the original image IA. Accordingly, inthis case, it is determined in S420 that the width of the distributionarea of the corrected image IB is not changed with respect to the widthof the distribution area of the original image IA.

When the width of the distribution area of the corrected image IB is notchanged with respect to the width of the distribution area of theoriginal image IA (S420: NO), the CPU 210 determines whether thedistribution area of the corrected image IB is shifted with respect tothe distribution area of the original image IA in the same direction,irrespective of the gradation values, in S430. For example, when it ispossible to determine that the shift direction of the low gradation-sidearea LA and the shift direction of the high gradation-side area HA arethe same, it is determined that the distribution area of the correctedimage IB is shifted in the same direction, irrespective of the gradationvalues. In this illustrative embodiment, when the shift direction of thecorresponding most frequent values of the corrected image IB withrespect to the most frequent values Rm, Gm, Bm of the original image IAis the same as the shift direction of the corresponding opposite-sidemost frequent values of the corrected image IB with respect to theopposite-side most frequent values Ro, Go, Bo of the original image IA,the CPU 210 determines that the distribution area of the corrected imageIB is shifted in the same direction, irrespective of the gradationvalues. Since the most frequent value and the opposite-side mostfrequent value are respectively positioned at one side and other side ofthe low gradation-side area LA and the high gradation-side area HA, itis possible to appropriately determine whether the distribution area ofthe corrected image IB is shifted in the same direction, irrespective ofthe gradation values, by using the shift directions of the most frequentvalue and the opposite-side most frequent value.

For example, when the third corrected image data is the processingtarget, as shown in FIG. 10A, the most frequent values Rm3, Gm3, Bm3 andthe opposite-side most frequent values Ro3, Go3, Bo3 of the thirdcorrected image IB3 are shifted in the same direction (the rightwarddirection in FIG. 10A). Accordingly, when the third corrected image datais the processing target, it is determined that the distribution area ofthe corrected image IB is shifted in the same direction, irrespective ofthe gradation values.

When the distribution area of the corrected image IB is shifted in thesame direction, irrespective of the gradation values (S430: YES), theCPU 210 presumes in S440 that the correspondence processing is theoverall brightness correction processing.

When the distribution area of the corrected image IB is not shifted withrespect to the distribution area of the original image IA (S410: NO),when the width of the distribution area of the corrected image IB ischanged with respect to the width of the distribution area of theoriginal image IA (S420: YES), or when the distribution area of thecorrected image IB is not shifted in the same direction (S430: NO), theCPU 210 presumes in S450 that the correspondence processing is not theoverall brightness correction processing.

According to the above-described overall brightness correctionpresumption processing of this illustrative embodiment, when the mostfrequent value and the opposite-side most frequent value as thecorresponding representative values of the corrected image IB areshifted in the same direction shifted with respect to each of the mostfrequent value and the opposite-side most frequent value as a pluralityof representative values of the original image IA (S430: YES) and thewidth of the distribution area of the corrected image IB is not changedwith respect to the width of the distribution area of the original imageIA (S420: NO), the correspondence processing is presumed as the overallbrightness correction processing. As a result, it is possible toappropriately determine whether the correspondence processing is theoverall brightness correction processing by using the plurality ofrepresentative values and the widths of the distribution areas of theoriginal image IA and the corrected image IB.

In case of the background removal processing, for example, while thedistribution area of the high gradation-side area HA corresponding tothe background is shifted, the distribution area of the lowgradation-side area LA corresponding to the object is not shifted (FIGS.9A and 11A). Also, in case of the ghosting removal processing, neitherthe distribution area corresponding to the background nor thedistribution area corresponding to the object is shifted (FIGS. 9B and11B). Also, in the contrast correction processing, the distribution areain the low gradation-side area LA and the high gradation-side area HAare shifted in the opposite directions (FIGS. 10B and 11D). In contrast,in the overall brightness correction processing, the distribution areain the low gradation-side area LA and the high gradation-side area HAare shifted in the same direction shifted (FIGS. 10A and 11C).

Also, in the overall brightness correction processing, the width of thedistribution area is not changed. The reason will be described. When agradient of the tone curve (FIG. 3) used for the correction is ‘1’, thewidth of the distribution area is not changed by the correction. On theother hand, when the gradient of the tone curve (FIG. 3) is less than 1,the width of the distribution area is reduced by the correction, andwhen the gradient is greater than 1, the width of the distribution areais increased by the correction. In the overall brightness correctionprocessing, since an average gradient of the tone curve is substantially‘1’, the width of the distribution area is little changed by thecorrection. For example, for the tone curve C3(3) of FIG. 3A used in theoverall brightness correction processing of this illustrativeembodiment, the gradient is greater than 1 in the range of 0≦Vin<Va, is1 in the range of Va≦Vin<Vb and is less than 1 in the range ofVb≦Vin<Vm. Therefore, it can be seen that the average gradient of thetone curve C3(3) is substantially ‘1.’

In this way, it is possible to precisely presume whether thecorrespondence processing is the overall brightness correctionprocessing by the overall brightness correction presumption processing.

A-3-7. Contrast Correction Presumption Processing

The contrast correction presumption processing of S150 in FIG. 7 will bedescribed. FIG. 15 is a flowchart of the contrast correction presumptionprocessing. In S510, the CPU 210 determines whether the distributionarea of the corrected image IB is shifted with respect to thedistribution area of the original image IA. Since this determination isthe same as the determination in S410 of FIG. 14, the determinationresult in S410 of FIG. 14 is used as it is.

When the distribution area of the corrected image IB is shifted withrespect to the distribution area of the original image IA (510: YES),the CPU 210 determines whether the width of the distribution area of thecorrected image IB is reduced with respect to the width of thedistribution area of the original image IA, in S520. Since thisdetermination is the same as the determination in S330 of FIG. 13, thedetermination result in S330 of FIG. 13 is used as it is.

When the width of the distribution area of the corrected image IB isreduced with respect to the width of the distribution area of theoriginal image IA (S520: YES), the CPU 210 determines whether thedistribution area of the corrected image IB is shifted with respect tothe distribution area of the original image IA in a different directionaccording to the gradation value, in S530. For example, when it ispossible to determine that the shift direction of the low gradation-sidearea LA is opposite to the shift direction of the high gradation-sidearea HA, it is determined that the distribution area of the correctedimage IB is shifted in a different direction according to the gradationvalue. In this illustrative embodiment, when the shift direction of thecorresponding most frequent values of the corrected image IB withrespect to the most frequent values Rm, Gm, Bm of the original image IAis opposite to the shift direction of the corresponding opposite-sidemost frequent values of the corrected image IB with respect to theopposite-side most frequent values Ro, Go, Bo of the original image IA,the CPU 210 determines that the distribution area of the corrected imageIB is shifted in a different direction according to the gradation value.Since the most frequent value and the opposite-side most frequent valueare respectively positioned at one side and other side of the lowgradation-side area LA and the high gradation-side area HA, it ispossible to appropriately determine whether the shift direction of thelow gradation-side area LA is opposite to the shift direction of thehigh gradation-side area HA by using the shift directions of the mostfrequent value and the opposite-side most frequent value.

For example, when the fourth corrected image data is the processingtarget, the most frequent values Rm4, Gm4, Bm4 of the fourth correctedimage IB4 are rightwards shifted and the opposite-side most frequentvalues Ro4, Go4, Bo4 are leftwards shifted, as shown in FIG. 10B.Accordingly, when the fourth corrected image data is the processingtarget, it is determined that the distribution area of the correctedimage IB is shifted in a different direction according to the gradationvalue.

When the distribution area of the corrected image IB is shifted in adifferent direction according to the gradation value (S530: YES), theCPU 210 presumes in S540 that the correspondence processing is thecontrast correction processing.

When the distribution area of the corrected image IB is not shifted withrespect to the distribution area of the original image IA (S510: NO),when the width of the distribution area of the corrected image IB is notreduced with respect to the width of the distribution area of theoriginal image IA (S520: NO), or when the distribution area of thecorrected image IB is not shifted in the different direction accordingto the gradation value (S530: NO), the CPU 210 presumes in S550 that thecorrespondence processing is not the contrast correction processing.

According to the above-described contrast correction presumptionprocessing of this illustrative embodiment, when the opposite-side mostfrequent value as the representative value in the length of the lowgradation-side area LA of the corrected image IB is shifted in a firstdirection (the leftward direction in the example of FIG. 10B) withrespect to the opposite-side most frequent value as the correspondingrepresentative value of the original image IA, the most frequent valueas the representative value in the high gradation-side area HA of thecorrected image IB is shifted in second direction (the rightwarddirection in the example of FIG. 10B), which is opposite to the firstdirection, with respect to the most frequent value as the correspondingrepresentative value of the original image IA (S530: YES), and the widthof the distribution area of the corrected image IB is reduced withrespect to the width of the distribution area of the original image IA(S520: YES), the correspondence processing is presumed as the contrastcorrection processing. As a result, it is possible to appropriatelypresume whether the correspondence processing is the contrast correctionprocessing by using the plurality of representative values and thedistribution width values of the original image IA and the correctedimage IB.

In case of the background removal processing, for example, while thedistribution area of the high gradation-side area HA corresponding tothe background is shifted, the distribution area of the lowgradation-side area LA corresponding to the object is not shifted (FIGS.9A and 11A). Also, in case of the ghosting removal processing, neitherthe distribution area corresponding to the background nor thedistribution area corresponding to the object is shifted (FIGS. 9B and11B). Also, in the overall brightness correction processing, thedistribution area in the low gradation-side area LA and the distributionarea in the high gradation-side area HA are shifted in the samedirection (FIGS. 10A and 11C). In contrast, in the contrast correctionprocessing, the distribution area in the low gradation-side area LA andthe distribution area in the high gradation-side area HA are shifted inthe opposite directions (FIGS. 10B and 11D).

Also, while the width of the distribution area is not changed in theoverall brightness correction processing, the width of the distributionarea may be reduced in the contrast correction processing. The reasonwill be described. The tone curve of the contrast correction processinghas a relatively wide part in which the gradient is less than 1. Forexample, for the tone curve C4(3) of FIG. 3B used for the contrastcorrection processing of this illustrative embodiment, a range(0≦Vin<Va, Vb≦Vin<Vm) in which the gradient is less than 1 is wider thana range (Va≦Vin<Vb) in which the gradient is greater than 1. Also, forthe tone curve C4(−3) of FIG. 3B, a range (Va≦Vin<Vb) in which thegradient is less than 1 is wider than a range (0≦Vin<Va, Vb≦Vin<Vm) inwhich the gradient is greater than 1. Accordingly, in the contrastcorrection processing, the width of the distribution area may bereduced, unlike the overall brightness correction processing. Meanwhile,in this illustrative embodiment, the condition for presuming that thecorrespondence processing is the contrast correction includes acondition ‘the width of the distribution area is reduced’. However, thecondition may not include a condition ‘the width of the distributionarea is reduced.’ However, in general, when the contrast correctionprocessing is performed, the width of the distribution area is reducedin many cases. Therefore, like this illustrative embodiment, when thecondition ‘the width of the distribution area is reduced’ is included inthe presumption condition, it is possible to improve the presumptionprecision.

In this way, it is possible to precisely presume whether thecorrespondence processing is the contrast correction processing by theabove-described contrast correction presumption processing.

Also, as can be seen from the descriptions of the overall brightnesscorrection presumption processing and the contrast correctionpresumption processing, in this illustrative embodiment, for each of theplurality of representative values (for example, the most frequent valueand the opposite-side most frequent value) of the original image IA, thecorrespondence processing is presumed by using, the direction in whichthe plurality of corresponding representative values of the correctedimage IB is shifted. In the processing of correcting the brightness ofthe image such as the overall brightness correction processing and thecontrast correction processing, the distribution area in the image isshifted according to the type of the correction. Accordingly, accordingto this illustrative embodiment, it is possible to appropriately presumethe correspondence processing from plural types of candidates forprocessing (for example, the overall brightness correction presumptionprocessing and the contrast correction presumption processing) ofcorrecting the brightness of the image.

Also, as can be seen from the descriptions of the four types of thepresumption processing, in this illustrative embodiment, as the firstindex value and the second index value, the representative values (forexample, the three most frequent values of the three color components)of the plurality of pixel values in the image, number of representativepixels (for example, number of most frequent pixels) indicative ofnumber of pixels having the representative values and the widths of thedistribution areas are calculated, and the correspondence processing ispresumed based on the shifts of the representative values between theoriginal image IA and the corrected image IB, the increase or decreaseof number of representative pixels, and the increase or decrease of thewidths of the distribution areas. As a result, based on therepresentative values, number of representative pixels and the widths ofthe distribution areas, it is possible to precisely presume thecorrespondence processing.

A-3-8. Correction Level Determination Processing

The correction level determination processing of S40 in FIG. 5 will bedescribed. FIG. 16 is a flowchart of the correction level determinationprocessing. In S610, the CPU 210 determines whether the correspondenceprocessing is presumed as the background removal processing by thecorrection type presumption processing of S30.

When the correspondence processing is presumed as the background removalprocessing (S610: YES), the CPU 210 terminates the correction leveldetermination processing. The reason is that there is no correctionlevel in the background removal processing, as described above.

When the correspondence processing is presumed not as the backgroundremoval processing (S610: NO), the CPU 210 determines whether thecorrespondence processing is presumed as the ghosting removal processingby the correction type presumption processing of S30, in S620.

When the correspondence processing is presumed as the ghosting removalprocessing (S620: YES), the CPU 210 determines the correction level L2,which is a parameter for the ghosting removal processing, by using anincrement of number of most frequent pixels, in S630. Specifically,increments (RmN2-RmN), (GmN2-GmN) and (BmN2-BmN) of number of mostfrequent pixels RmN2, GmN2, BmN2 of the second corrected image IB2 withrespect to number of most frequent pixels RmN, GmN, BmN of therespective color components of the original image IA are respectivelycalculated. An average value AD of the increments is calculated. Then,the correction level L2 is determined so that the greater the averagevalue AD of the increments, the correction level L2 becomes higher. Forexample, the correction level L2 is determined by referring to a tablein which a correspondence relation between the average value AD of theincrements and the correction level L2 is defined in advance.

In this way, according to the correction level determination processingof this illustrative embodiment, the correction level L2 is determinedusing the increment of number of most frequent pixels, i.e., adifference between number of most frequent pixels RmN, GmN, BmNindicative of number of pixels having the background color value of theoriginal image IA and number of most frequent pixels RmN2, GmN2, BmN2indicative of number of pixels having the background color value of thecorrected image IB. As a result, it is possible to appropriatelydetermine the correction level L2 for the ghosting removal processing.More specifically, in the ghosting removal processing, as describedabove, the greater the correction level L2, the specific width ΔV2indicative of an area of the background-approximate range RA2 (FIG. 2)becomes greater. Accordingly, the greater the correction level L2,number of pixels becoming the correction target increases, so that theincrement of number of most frequent pixels increases. Accordingly, thegreater the increment of number of most frequent pixels of the correctedimage IB by the specific correction processing, the correction level L2of the ghosting removal processing presumed as the correspondenceprocessing is preferably set to be higher. Thereby, when executing theghosting removal processing as the presumed correspondence processing atthe second multifunction machine 200, it is possible to preciselyreproduce the specific correction processing executed at the firstmultifunction machine 100.

When the correspondence processing is presumed not as the ghostingremoval processing (S620: NO), the CPU 210 determines whether thecorrespondence processing is presumed as the overall brightnesscorrection processing by the correction type presumption processing ofS30, in S640.

When the correspondence processing is presumed as the overall brightnesscorrection processing (S640: YES), the CPU 210 determines the correctionlevel L3, which is a parameter for the overall brightness correctionprocessing, by using a shift amount of the distribution area, in S650.Specifically, the CPU 210 selects one value, which can be preferablyused to determine the correction level L3, of the most frequent valuesRm, Gm, Bm and the opposite-side most frequent values Ro, Go, Bo of therespective color components of the original image IA. For example, ascan be seen from the tone curve C3 of FIG. 3A, the correction amount isnot constant in the vicinity of the minimum value (0, in thisillustrative embodiment) or the maximum value (255, in this illustrativeembodiment) of the range of the values that can be taken as thegradation value. Therefore, the value in the vicinity of the minimumvalue or the maximum value is not preferably used to determine thecorrection level L3. Accordingly, a value closest to an intermediatevalue (128, in this illustrative embodiment) of the range of the valuesthat can be taken as the gradation value is selected. The CPU 210calculates, as the shift amount, a difference between the selected valueof the original image IA and the corresponding value of the correctedimage IB. Then, the correction level L3 is determined so that thegreater the calculated shift amount, the correction level L3 becomeshigher. For example, the correction level L3 is determined by referringto a table in which a correspondence relation between the shift amountand the correction level L3 is defined in advance.

When the correspondence processing is presumed not as the overallbrightness correction processing (S640: NO), the CPU 210 determineswhether the correspondence processing is presumed as the contrastcorrection processing by the correction type presumption processing ofS30, in S660.

When the correspondence processing is presumed as the contrastcorrection processing (S660: YES), the CPU 210 determines the correctionlevel L4, which is a parameter for the contrast correction processing,by using the shift amount of the distribution area, in S670.Specifically, the CPU 210 selects one value, which can be preferablyused to determine the correction level L4, of the most frequent valuesRm, Gm, Bm and the opposite-side most frequent values Ro, Go, Bo of therespective color components of the original image IA. For example, ascan be seen from the tone curve C4 of FIG. 3B, the correction amount islargely varied in the vicinity of the minimum value (0, in thisillustrative embodiment) or the maximum value (255, in this illustrativeembodiment) of the range of the values that can be taken as thegradation value and in the vicinity of the intermediate value (128, inthis illustrative embodiment) of the range of the values that can betaken as the gradation value. Therefore, the value in the vicinity ofthe minimum value, the maximum value or the intermediate value is notpreferably used to determine the correction level L4. Accordingly, avalue closest to any one of values (64 and 192, in this illustrativeembodiment) of 25% and 75% from the minimum value of the range of thevalues that can be taken as the gradation value is selected. The CPU 210calculates, as the shift amount, a difference between the selected valueof the original image IA and the corresponding value of the correctedimage IB. Then, the correction level L4 is determined so that thegreater the calculated shift amount, the correction level L4 becomeshigher. For example, the correction level L4 is determined by referringto a table in which a correspondence relation between the shift amountand the correction level L4 is defined in advance.

In this way, according to the correction level determination processingof this illustrative embodiment, the correction levels L3, L4 aredetermined using the shift amount of the corresponding representativevalue of the corrected image IB with respect to the specificrepresentative value (in this illustrative embodiment, one valueselected from the most frequent values Rm, Gm, Bm and the opposite-sidemost frequent values Ro, Go, Bo) of the original image IA. As a result,it is possible to appropriately determine the correction levels L3, L4for the overall brightness correction processing and the contrastcorrection processing. More specifically, in the overall brightnesscorrection processing and the contrast correction processing, asdescribed above, the greater the correction levels L3, L4, thecorrection amounts ΔV3, ΔV4 become greater (FIG. 3). As a result, thegreater the correction levels L3, L4, the shift amount of the value ofthe pixel by the correction becomes greater. Accordingly, the greaterthe shift amount of the value of the pixel of the corrected image IB bythe specific correction processing, the correction levels L3, L4 of theoverall brightness correction processing and the contrast correctionprocessing presumed as the correspondence processing are preferably setto be higher. Thereby, when executing the overall brightness correctionprocessing and the contrast correction processing as the presumedcorrespondence processing at the second multifunction machine 200, it ispossible to precisely reproduce the specific correction processingexecuted at the first multifunction machine 100.

When the correspondence processing is presumed not as the contrastcorrection processing (S660: NO), this means that the correspondenceprocessing cannot be presumed by the correction type presumptionprocessing of S30. Therefore, the correction level determinationprocessing is terminated without determining the correction level.

A-3-9. Scan Processing:

Subsequently, the scan processing, which is to be executed by the CPU210 of the second multifunction machine 200 after the correctioninformation registration processing, will be described. In the scanprocessing, the correction processing can be executed for the scan databy using the correction information registered in the correctioninformation registration processing. The scan processing starts inresponse to a user's instruction. FIG. 17 is a flowchart of the scanprocessing.

In S810, the CPU 210 displays, on the display unit 270, an instructioninput screen WI2 with which the user inputs an instruction relating tothe scan processing. FIG. 18 illustrates an example of the instructioninput screen WI2 and a detail setting screen WI3. The instruction inputscreen WI2 of FIG. 18A includes a pull-down menu PM and buttons BT3,BT4. The pull-down menu PM is an input element for inputting a selectioninstruction to select one correction information, which indicates thecorrection processing to be executed for the scan data, from theregistered plurality of correction information.

Each of plurality of items that can be selected from the pull-down menuPM indicates a registration name of the corresponding correctioninformation. The user can easily input the selection instruction of onecorrection information by selecting one registration name from thepull-down menu PM. The plurality of selectable correction informationincludes N correction information registered by executing N times (N: aninteger of 2 or greater) the correction information registrationprocessing of FIG. 5. In the N times correction information registrationprocessing, the combinations of N sets of the original image data andthe corrected image data are used. Each of the combinations of N sets isa combination of one image data of N different first original image dataand one first corrected image data corresponding to the one image data,for example. Therefore, it can be said that the selection instruction ofone correction information is a selection instruction to select oneprocessing to be executed from the N correspondence processing presumedfor the combinations of N sets. Also, the plurality of selectablecorrection parameters may include one or more default correctioninformation registered in advance. In the meantime, the CPU 210 mayassociate and store the used original image data and the corrected imagedata when registering the correction information in S70 of FIG. 5, orimage data representing respective thumbnail images of the originalimage IA and the corrected image IB and the correction information inthe volatile storage device 230. In this case, the CPU 210 may display,on the instruction input screen WI2 of FIG. 18A, the original image IAand the corrected image IB corresponding to the correction informationselected from the pull-down menu PM or the thumbnail images of theimages. By doing so, when the user selects the registered correctioninformation, the user can easily check the contents of the correctionprocessing to be executed on the instruction input screen WI2.Particularly, this configuration is convenient when the user havingregistered the correction information is different from the userexecuting the correction processing based on the correction informationor when the plurality of correction information is registered, forexample.

When a detail setting button BT5 is pressed, the CPU 210 displays adetail setting screen WI3 of FIG. 18B on the display unit 270.Specifically, the detail setting screen WI3 includes a check box CB1,three slide bars SB2 to SB4 and two buttons BT5, BT6. The check box CB1is a UI element for inputting an instruction as to whether or not toexecute the background removal processing. The slide bar SB2 is a UIelement for inputting the correction level L2 of the ghosting removalprocessing. When a slide of the slide bar SB2 is located at a left endposition, it indicates that the ghosting removal processing is not to beexecuted (i.e., the correction level L2=0). The slide bars SB3, SB4 areUI elements for inputting the correction levels L3, L4 of the overallbrightness correction processing and the contrast correction processing.When sliders of the slide bars SB3, SB4 are located at centralpositions, it indicates that the overall brightness correctionprocessing and the contrast correction processing are not to be executed(i.e., the correction level L3, L4=0). In the detail setting screen WI3,the setting contents based on the correction information selected fromthe pull-down menu PM of the instruction input screen WI4 displayed justpreviously are shown. For example, in the example of FIG. 18B, thesetting with which the background removal processing, the ghostingremoval processing and the overall brightness correction processing arenot to be executed and the contrast correction of making the contrasthigh is to be executed is shown. By seeing the detail setting screenWI3, the user can recognize the manual setting method for executing thecorrection processing based on the registered correction information,i.e., the same correction processing as the specific correctionprocessing executed for the original image data at the firstmultifunction machine 100. Further, the user can adjust the setting ofthe correction processing by operating the check box CB1 and the slidebars SB2 to SB4.

When the user presses the detail setting button BT5, the CPU 210activates the settings of the correction processing on the detailsetting screen WI3 at that time and returns to the state where theinstruction input screen WI2 is displayed. When the user presses acancel button BT6, the CPU 210 returns to the state where theinstruction input screen WI2 is displayed.

When a start button BT3 of the instruction input screen WI2 is pressed,the CPU 210 proceeds to S820. When a cancel button BT4 of theinstruction input screen WI2 is pressed, the CPU 210 terminates the scanprocessing.

In S820, the CPU 210 generates the scan data of the processing target byreading the document with the scanner unit 250, thereby acquiring thescan data of the processing target. Instead, for example, the scan dataof the processing target may be acquired from one or more scan datagenerated in advance by reading documents with the scanner unit 250 andstored in the non-volatile storage device 220. The acquired scan data isstored in the buffer area 231.

In S830, the CPU 210 acquires the correction information selected fromthe pull-down menu PM upon the pressing of the start button BT3 of theinstruction input screen WI2 from the volatile storage device 230. Thatis, one correction information selected by the selection instructioninput by the user through the pull-down menu PM is acquired. Asdescribed above, the correction information includes the informationindicative of the type of the correction processing (in thisillustrative embodiment, any one of the background removal processing,the ghosting removal processing, the overall brightness correctionprocessing and the contrast correction processing) and the informationindicative of the correction level.

In S840, the CPU 210 determines the type of the correction processing tobe executed based on the acquired correction information. In S840, theCPU 210 determines the correction level of the correction processing tobe executed based on the acquired correction information.

In S860, the CPU 210 executes the correction processing of the typedetermined in S840 for the scan data of the processing target with thecorrection level determined in S850, thereby generating the correctedscan data. The specific correction processing is as described above withreference to FIGS. 2 and 3.

In S870, the CPU 210 outputs the corrected image data. Specifically, theCPU 210 outputs the corrected image data to the volatile storage device230, and preserves the second corrected image data in the volatilestorage device 230. Instead, the output of the corrected image data mayalso be made by transmitting the corrected image data to the user'sterminal, or controlling the printer unit 240 to print the correctedimage by using the corrected image data.

According to the above-described scan processing of this illustrativeembodiment, the scan data representing the image of the correctiontarget is acquired (S820). The correspondence processing presumed in thecorrection type presumption processing, i.e., the correction processingdetermined in S840 based on the registered correction information isexecuted for the scan data (S860). As a result, it is possible to easilyexecute the correspondence processing to the specific correctionprocessing, which has been executed for the first original image data,for the scan data. Therefore, it is possible to reduce the trouble ofthe user who uses the correction processing.

Further, the instruction input screen WI2, which includes the pull-downmenu PM for inputting the selection instruction to select one correctionprocessing to be executed, is displayed on the display unit 270 (S810).Then, one correction processing selected by the selection instruction isexecuted for the scan data (S840 to S860). Therefore, it is possible toexecute the appropriate processing for the scan data from the pluralityof correspondence processing presumed in the correction informationregistration processing of FIG. 5, based on the user's instruction.

Also, according to the above illustrative embodiment, in theregistration processing of FIG. 5, based on the comparison of theoriginal image data and the corrected image data, the correspondenceprocessing to the specific correction processing executed for theoriginal image data is presumed from plural types of candidates forprocessing that can be executed by the first multifunction machine 100(S30 in FIG. 5), and the correction level, which is a parameter for thepresumed correspondence processing, is determined (S40 in FIG. 5). Then,in the scan processing of FIG. 17, the presumed correspondenceprocessing is executed for the scan data by using the determinedcorrection level. As a result, it is possible to reduce the user'strouble to set the correction level and the type of the correctionprocessing at the image processing apparatus.

B. Modified Embodiments

(1) In S410 of FIG. 14 and S510 of FIG. 15 of the illustrativeembodiment, the representative values of the original image IA and thecorrected image IB, which are to be used to determine whether thedistribution area is shifted, are the most frequent value and theopposite-side most frequent value of each color component. However, thisdisclosure is not limited thereto. For example, the intermediate values,the minimum values and the maximum values of the two distribution areas(for example, the two distribution areas RDo, RDb of the R component inFIG. 8B) of each color component of the original image IA may be used asthe representative values of the original image IA and the correctedimage IB. Alternatively, the histograms of the respective brightness ofthe original image IA and the corrected image IB may be generated, andthe most frequent values and the opposite-side most frequent values ofthe histograms of the brightness may be used as the representativevalues of the original image IA and the corrected image IB.

(2) In the correction type presumption processing of the illustrativeembodiment, the correspondence processing is presumed by using, all ofthe shifts of the most frequent value and the opposite-side mostfrequent value, the increase or decrease of number of most frequentpixels and the increase or decrease of the width of the distributionarea. Instead, for example, the correspondence processing may bepresumed based on only one of the shift of the opposite-side mostfrequent value, the increase or decrease of number of most frequentpixels and the increase or decrease of the width of the distributionarea. For example, when the processing becoming the candidate of thecorrespondence processing is only the overall brightness correctionprocessing and the contrast correction processing, it is possible topresume whether the correspondence processing is the overall brightnessor the contrast correction processing just by determining whether theshift directions of the most frequent value and the opposite-side mostfrequent value are the same direction or the opposite directions. Forexample, when the processing becoming the candidate of thecorrespondence processing is only the background removal processing andthe ghosting removal processing, it is possible to presume whether thecorrespondence processing is the background removal processing or theghosting removal processing just by determining whether the mostfrequent value is not shifted or whether the most frequent value isshifted to the position indicative of white.

(3) In the ghosting removal presumption processing of FIG. 13, thenecessary condition for presuming that the correspondence processing isthe ghosting removal processing is both the conditions that number ofmost frequent pixels is increased and that the width of the distributionarea is decreased. Instead, it may be determined as to whether number ofmost frequent pixels is increased or whether the width of thedistribution area is decreased, and a condition that any one conditionis satisfied may be set as the necessary condition for presuming thatthe correspondence processing is the ghosting removal processing. Ingeneral, when number of most frequent pixels is increased, the width ofthe distribution area is decreased, and when the width of thedistribution area is decreased, number of most frequent pixels isincreased. Therefore, even any one condition may be sufficient in somecases.

(4) In the illustrative embodiment, the correction levels L2 to L4 aredetermined as the correction parameter in the correction leveldetermination processing. However, the other parameters may bedetermined. For example, instead of the correction level L2, thespecific width ΔV2 of the ghosting removal processing may be determinedas the correction parameter. Also, instead of the correction levels L3,L4, the correction amounts ΔV3, ΔV4 of the overall brightness correctionprocessing and the contrast correction processing may be determined asthe correction parameter.

(5) In the illustrative embodiment, the reading apparatus configured togenerate the original image data and the apparatus configured to executethe specific correction processing for the original image data and togenerate the corrected image data are the same apparatus (i.e., thefirst multifunction machine 100) but may be different. For example, theoriginal image data generated by the first multifunction machine 100 maybe supplied to the user's terminal (for example, a PC and a smart phone)and a CPU of the terminal may execute the specific correctionprocessing. In this case, for example, the original image data and thecorrected image data may be transmitted from the terminal to the secondmultifunction machine 200 through the communication unit 180 and may bethus acquired by the second multifunction machine 200.

(6) In the illustrative embodiment, the reading apparatus (i.e., thefirst multifunction machine 100) configured to generate the scan data ofthe correction target acquired in S820 of FIG. 18 and the readingapparatus configured to execute the presumed correspondence processingfor the scan data are the same apparatus (i.e., the second multifunctionmachine 200) but may be different. For example, the scan data of thecorrection target generated by the second multifunction machine 200 maybe supplied to the user's terminal, and the CPU of the terminal mayexecute the presumed correspondence processing. In this case, the CPU ofthe terminal may acquire the original image data and the corrected imagedata from the first multifunction machine 100, execute the correctioninformation registration processing, acquire the scan data of thecorrection target from the second multifunction machine 200, and executethe presumed correspondence processing.

(7) In the illustrative embodiment, the reading apparatus (i.e., thefirst multifunction machine 100) configured to generate the originalimage data and the reading apparatus (i.e., the second multifunctionmachine 200) configured to generate the scan data of the correctiontarget are different but may be the same. For example, the originalimage data may be generated at the second multifunction machine 200 andsupplied to the user's terminal, and the CPU of the terminal may executethe specific correction processing. In this case, the CPU 210 may beconfigured to execute the correction information registration processingso that the CPU 210 of the second multifunction machine 200 can executethe same processing as the specific correction processing for the scandata of the correction target, which is generated at the secondmultifunction machine 200.

(8) In the illustrative embodiment, the reading apparatus (i.e., thefirst multifunction machine 100) configured to execute the specificcorrection processing and the reading apparatus (i.e., the secondmultifunction machine 200) configured to execute the presumedcorrespondence processing are different but may be the same. Forexample, it is assumed that the correction information of the specificcorrection processing, which was executed for the previously generatedoriginal image data at the second multifunction machine 200, is deleted.In this case, the CPU 210 may be configured to execute the correctioninformation registration processing so that the CPU 210 of the secondmultifunction machine 200 can execute the same processing as thepreviously executed specific correction processing for the scan data ofthe correction target.

(9) In the illustrative embodiment, the original image data is the scandata generated by the scanner unit 250. This disclosure is not limitedthereto, and a variety of image data representing the optically readimage may be adopted. For example, the image data, which is generatedwhen a document is optically read by the photographing of a digitalcamera, may be adopted. Also, the original image data is not limited tothe image data generated by the reading apparatus (scanner or digitalcamera), and may be image data prepared using an application programsuch as drawing preparation and document preparation.

(10) In the illustrative embodiment, a part of the configurations thatare implemented by the hardware may be replaced with the software. Also,a part or all of the configurations that are implemented by the softwaremay be replaced with the hardware.

Although this disclosure has been described based on the illustrativeembodiments and the modified embodiments, the illustrative embodimentsare provided only to easily understand this disclosure, not to limitthis disclosure. This disclosure can be changed and improved withoutdeparting from the gist and scope of this disclosure and includes theequivalents thereto.

What is claimed is:
 1. An image processing apparatus comprising acontroller configured to perform: acquiring original image datarepresenting an original image and corrected image data representing acorrected image, the corrected image data being generated by executingspecific correction processing on the original image data; calculating afirst index value and a second index value, the first index valuerelating to a color distribution of the original image by using theoriginal image data, and the second index value relating to a colordistribution of the corrected image by using the corrected image data;and determining which processing, among a plurality of processingcandidates that are executable by the image processing apparatus,corresponds to the specific correction processing, based on comparisonof the calculated first index value and the calculated second indexvalue.
 2. The image processing apparatus according to claim 1, wherein,to perform the calculating, the controller is configured to perform:generating a first histogram indicative of a distribution of pluralityof pixel values in the original image by using the original image data;calculating the first index value by using the first histogram;generating a second histogram indicative of a distribution of pluralityof pixel values in the corrected image by using the corrected imagedata; and calculating the second index value by using the secondhistogram.
 3. The image processing apparatus according to claim 2,wherein, to perform the calculating, the controller is configured toperform calculating, as the first index value and the second indexvalue, at least one of a (a) representative value of plurality of pixelvalues in an image, (b) number of representative pixels, which indicatesnumber of pixels having the representative value, of the plurality ofpixels in the image, and (c) a distribution width value, which indicatesa width of a range of pixels, number of which is equal to or greaterthan a reference number, of the plurality of pixels in the image, andwherein, to perform the determining, the controller is configured toperform determining the processing corresponding to the specificcorrection processing, based on at least one of a shift of therepresentative value between the original image and the corrected image,an increase or decrease of number of representative pixels and anincrease or decrease of the distribution width value.
 4. The imageprocessing apparatus according to claim 3, wherein the plurality ofprocessing candidates comprises a plurality of processings of correctinga background color in the image, wherein, to perform the calculating,the controller is configured to perform calculating, as therepresentative value, a most frequent value of the plurality of pixelvalues in the image for each of the original image and the correctedimage, and wherein, to perform the determining, the controller isconfigured to perform determining the processing corresponding to thespecific correction processing by using the respective most frequentvalues of the original image and the corrected image.
 5. The imageprocessing apparatus according to claim 3, wherein the plurality of theprocessing candidates comprises a first processing of changing abackground color in the image into a specific color different from thebackground color, wherein, to perform the calculating, the controller isconfigured to perform calculating a background color value, which is therepresentative value indicative of the background color of the image,for each of the original image and the corrected image, and wherein, toperform the determining, the controller is configured to performdetermining that the processing corresponding to the specific correctionprocessing is the first processing, if the background color value of theoriginal image indicates a color different from the specific color andthe background color value of the corrected image indicates the specificcolor.
 6. The image processing apparatus according to claim 3, whereinthe plurality of the processing candidates comprises a second processingof changing a color of a background in an image into a representativecolor based on a value of a pixel in the background, wherein, to performthe calculating, the controller is configured to perform calculating abackground color value, which is the representative value indicative ofthe color of the background in the image, and at least one of number ofbackground color pixels, which is number of representative pixelsindicative of the background color value, and the distribution widthvalue, for each of the original image and the corrected image, andwherein, to perform the determining, the controller is configured toperform determining that the processing corresponding to the specificcorrection processing is the second processing, in at least one ofcases: (a) where the background color value of the corrected image doesnot shift with respect to the background color value of the originalimage and number of background color pixels of the corrected imageincreases with respect to number of background color pixels of theoriginal image; and (b) where the background color value of thecorrected image does not shift with respect to the background colorvalue of the original image and the distribution width value of thecorrected image decreases with respect to the distribution width valueof the original image.
 7. The image processing apparatus according toclaim 3, wherein the plurality of the processing candidates comprises aplurality of processings of correcting brightness of the image, wherein,to perform the calculating, the controller is configured to performcalculating a plurality of representative values for each of theoriginal image and the corrected image, and wherein, to perform thedetermining, the controller is configured to perform determining theprocessing corresponding to the specific correction processing by usinga direction in which the corresponding representative value of thecorrected image shifts with respect to each of the plurality ofrepresentative values of the original image.
 8. The image processingapparatus according to claim 7, wherein the plurality of the processingcandidates of candidates for processing comprises third processing ofincreasing or decreasing brightness of the whole image, wherein, toperform the calculating, the controller is configured to performcalculating the plurality of representative values and the distributionwidth value for each of the original image and the corrected image, andwherein, to perform the determining, the controller is configured toperform determining that the processing corresponding to the specificcorrection processing is the third processing, if the correspondingrepresentative value of the corrected image shifts in the same directionwith respect to each of the plurality of representative values of theoriginal image and the distribution width value of the corrected imagedoes not change with respect to the distribution width value of theoriginal image.
 9. The image processing apparatus according to claim 7,wherein the plurality of the processing candidates comprises fourthprocessing of changing contrast of the image, wherein, to perform thecalculating, the controller is configured to perform calculating theplurality of representative values and the distribution width value foreach of the original image and the corrected image, and wherein, toperform the determining, the controller is configured to performdetermining that the processing corresponding to the specific correctionprocessing is the fourth processing, in a case: where a first value in afirst range, in which the brightness is relatively low, of the pluralityof representative values of the corrected image shifts in a firstdirection with respect to a corresponding value of the plurality ofrepresentative values of the original image; and where a second value ina second range, in which the brightness is relatively high, of theplurality of representative values of the corrected image shifts in asecond direction opposite to the first direction with respect to acorresponding value of the plurality of representative values of theoriginal image.
 10. The image processing apparatus according to claim 1,wherein the controller further performs: determining a correctionparameter for the processing corresponding to the specific correctionprocessing, the specific correction processing being determined amongthe plurality of the processing candidates, by using a methodcorresponding to the processing to the specific correction processing.11. The image processing apparatus according to claim 10, wherein theplurality of the processing candidates comprises second processing ofchanging a color of a background in an image into a representative colorbased on a value of a pixel in the background, and wherein, to performthe determining, the controller is configured to perform determining acorrection parameter for the second processing by using a differencebetween number of representative pixels indicative of number of pixels,which have a representative value indicative of a background color ofthe original image, and number of representative pixels indicative ofnumber of pixels, which have a representative value indicative of abackground color of the corrected image.
 12. The image processingapparatus according to claim 10, wherein the plurality of the processingcandidates comprises at least one of third processing of increasing ordecreasing brightness of an whole image and fourth processing ofchanging contrast of the image, and wherein, to perform the determining,the controller is configured to perform determining a correctionparameter for one of the third processing and the fourth processing byusing a shift amount of a corresponding representative value of thecorrected image with respect to a specific representative value of theoriginal image.
 13. The image processing apparatus according to claim10, wherein the controller further performs: displaying a parameterdisplay screen for displaying the determined correction parameter to auser; and adjusting the determined correction parameter, based on anadjustment instruction to be input through the parameter display screen.14. The image processing apparatus according to claim 1, wherein thecontroller further performs notifying a user of the processingcorresponding to the specific correction processing presumed from theplurality of the processing candidates.
 15. The image processingapparatus according to claim 1, wherein, to perform the acquiring, thecontroller is configured to perform acquiring target image dataindicative of an image of a correction target; and wherein thecontroller further performs executing the processing corresponding tothe specific correction processing determined among the plurality of theprocessing candidates for the target image data.
 16. The imageprocessing apparatus according to claim 15, wherein, to perform thedetermining, the controller is configured to perform determining theprocessing corresponding to N specific correction processingrespectively corresponding to combinations of N sets of the originalimage data and the corrected image data, wherein the controller furtherperforms displaying an instruction input screen for inputting aselection instruction to select one of the N processing presumed for thecombinations of the N sets, and wherein, to perform the executing, thecontroller is configured to perform executing the one processingselected by the selection instruction.
 17. The image processingapparatus according to claim 15, wherein both of the original image dataand the corrected image data are generated by an apparatus differentfrom the image processing apparatus, and wherein the target image datais generated by the image processing apparatus.
 18. A non-transitorycomputer-readable medium having instructions to control a computer toperform: acquiring original image data representing an original imageand corrected image data representing a corrected image, the correctedimage data being generated by executing specific correction processingfor the original image data; calculating a first index value and asecond index value, the first index value relating to a colordistribution of the original image by using the original image data, andthe second index value relating to a color distribution of the correctedimage by using the corrected image data; and determining whichprocessing, among a plurality of processing candidates that areexecutable by the image processing apparatus, corresponds to thespecific correction processing, based on comparison of the calculatedfirst index value and the calculated second index value.